QJIT#

qjit(function=None, device=None)[source]#

Decorator to leverage the jasp + Catalyst infrastructure to compile the given function to QIR and run it on the Catalyst QIR runtime.

Parameters:

functioncallable: A function performing Qrisp code.
deviceobject: The PennyLane device to execute the function. The default device is “lightning.qubit”, a fast state-vector qubit simulator.

Returns:

callable: A function executing the compiled code.

Notes

Lightning-GPU is compatible with systems featuring NVIDIA Volta (SM 7.0) GPUs or newer. It is specifically optimized for Linux environments on X86-64 or ARM64 architectures running CUDA-12.

To install Lightning-GPU with NVIDIA CUDA support, the following packages need to be installed

pip install custatevec_cu12
pip install pennylane-lightning-gpu

Pre-built wheels for Lightning-AMDGPU are available for AMD MI300 series GPUs and systems running ROCm 7.0 or newer.

pip install pennylane-lightning-amdgpu

If the setup uses an older version of ROCm or a different AMD GPU series, Lightning-AMDGPU must be built manually from source.

Installation instructions for different platforms are available at pennylane.ai/install.

Examples

We write a simple function using the QuantumFloat quantum type and execute via qjit:

from qrisp import *
from qrisp.jasp import qjit

@qjit
def test_fun(i):
    qv = QuantumFloat(i, -2)
    with invert():
        cx(qv[0], qv[qv.size-1])
        h(qv[0])
    meas_res = measure(qv)
    return meas_res + 3

We execute the function a couple of times to demonstrate the randomness

>>> test_fun(4)
[array(5.25, dtype=float64)]
>>> test_fun(5)
[array(3., dtype=float64)]
>>> test_fun(5)
[array(7.25, dtype=float64)]

For executing on “lightning.gpu” we specify the device:

import pennylane as qml
from qrisp import *
from qrisp.jasp import qjit

dev = qml.device("lightning.gpu", wires=0)

@qjit(device=dev)
def test_fun(i):
    qv = QuantumFloat(i, -2)
    with invert():
        cx(qv[0], qv[qv.size-1])
        h(qv[0])
    meas_res = measure(qv)
    return meas_res + 3